1. Journey into data
Hi all, and welcome to this case study. I am Giniya, and I will be your instructor.
2. What is a case study?
A case study involves an in-depth analysis of a specific real-world situation using data. In this course, you will use Alteryx to turn data into valuable insights to answer specific business questions. We suggest that you complete these courses before working on this case study.
3. Data analysis flow in Alteryx
The data analysis flow has five different steps. You'll learn how to apply them using Alteryx.
4. Data analysis flow in Alteryx
The first step is to understand the business question. This will help you to determine the objective of the data analysis. For example, in a business scenario, the question could be, 'Which days of the week does the usage of smart fitness apps decrease, and how can we prevent it?'
5. Data analysis flow in Alteryx
After understanding the business question, it's crucial to do a data quality check, as raw data is often messy. It's essential to check data for missing values and duplicate rows and ensure it aligns with other internal data sources. You will build your first Alteryx workflow in this step!
6. Data analysis flow in Alteryx
The third step involves cleaning and processing the data by removing duplicate rows, replacing NULL values, and ensuring the correct data type is assigned to each field. It's important to clean data, as using inconsistent data will lead to flawed insights.
7. Data analysis flow in Alteryx
After finishing the data cleaning, you dig deeper into the data to reveal patterns and connections that can answer business-related questions. If required, you create calculated fields and combine datasets to obtain a better understanding.
8. Data analysis flow in Alteryx
Although no visualizations will be created in this course, I will compile the findings for a comprehensive case study discussion.
9. The scenario
In this case study, we will examine a situation where Bellabeat, a company that designs smart fitness-tracking products for women, seeks to expand its customer base. They have hired you to analyze consumer data from another brand. Bellabeat can utilize this information to enhance its products and develop a more effective marketing strategy. Let's review the data.
10. The data
The data you will be working with includes personal fitness tracker data from 30 users over 31 days.
The data consists of three CSV files.
Each dataset contains two common columns: ID and Date. ID is a unique identifier for an individual user
11. The data
The DailyActivity.csv file has 15 columns containing daily physical activity tracking data, including ID, activity date, calories, and other metrics such as total steps, distance, and sedentary and active minutes.
12. The data
The file SleepDay.csv contains daily sleep data in five columns, including ID, SleepDay, TotalMinutesAsleep, TotalTimeInBed, and other sleep-related metrics.
WeightLogInfo.csv includes a daily log of weight measurements in seven columns, including ID, date, and other weight-related metrics such as weight, height, and body fat.
13. Business questions
Now that you have seen the data let's examine the business questions.
Bellabeat aims to grow its customer base by revamping its marketing strategy and products. To identify the required changes, we will explore these questions:
- Is there any pattern in users' activity/exercise schedules?
- What are the most tracked activities?
- What is the predominant lifestyle based on the lifestyle index?
and
- What are the users' physical activity levels compared to the recommended norms?
14. Let's start analyzing!
It's time to check your understanding of the concepts introduced so far. Let's start analyzing!